iSQI CTFL_Syll_4.0 Exam With Confidence Using Practice Dumps

White-box testing, also known as structural testing, involves testing the internal structures or workings of an application, as opposed to its functionality (which is tested by black-box testing). The correct statement about white-box testing is that it helps find defects by measuring aspects such as statement coverage.

Statement Coverage: White-box testing techniques like statement coverage measure whether each statement in the code has been executed at least once. This helps ensure that all parts of the code are tested and can reveal defects in areas that might not be reached by black-box testing alone​​.

Other statements are less accurate in the context of white-box testing:

Specifications being vague: White-box testing is code-focused, not requirement-focused. If specifications are vague, it affects both white-box and black-box testing. The main advantage of white-box testing is that it allows testers to create tests based on the code's structure and logic​​.

Requirements-based coverage: This is typically associated with black-box testing, which derives tests from specifications and requirements. White-box testing, on the other hand, derives tests from the code itself​​.

Focus on defects rather than failures: Both white-box and black-box testing aim to identify defects, but white-box testing does this through code coverage and examining the code paths directly. It does not focus exclusively on defects rather than failures; it is just another method to identify potential issues​​.

Statement coverage is a structural coverage metric that measures the percentage of executable statements in the source code that are executed by a test suite1. Decision coverage is another structural coverage metric that measures the percentage of decision outcomes (such as branches or conditions) in the source code that are executed by a test suite1. Decision coverage is a stronger metric than statement coverage, because it requires that every possible outcome of each decision is tested, while statement coverage only requires that every statement is executed at least once2. Therefore, if a test suite achieves 100% decision coverage, it also implies that it achieves 100% statement coverage, because every statement in every branch or condition must have been executed. However, the converse is not true: 100% statement coverage does not guarantee 100% decision coverage, because some branches orconditions may have multiple outcomes that are not tested by the test suite2. For example, consider the following pseudocode:

if (x > 0) then print(“Positive”) else print(“Non-positive”) end if

A test suite that executes this code with x = 1 and x = -1 will achieve 100% statement coverage, because both print statements are executed. However, it will not achieve 100% decision coverage, because the condition x > 0 has only been tested with two outcomes: true and false. The third possible outcome, x = 0, has not been tested by the test suite. Therefore, the test suite may miss a potential bug or error in the condition or the branch.

The other options, such as stale transition coverage, equivalence class coverage, and boundary value coverage, are not guaranteed to be 100% by achieving 100% decision coverage. Stale transition coverage is a structural coverage metric that measures the percentage of transitions between states in a state machine that are executed by a test suite3. Equivalence class coverage is a functional coverage metric that measures the percentage of equivalence classes (or partitions) of input or output values that are tested by a test suite4. Boundary value coverage is another functional coverage metric that measures the percentage of boundary values (or extreme values) of input or output ranges that are tested by a test suite4. These metrics are independent of decision coverage, because they are based on different aspects of the system under test, such as its behavior, functionality, or specification. Therefore, achieving 100% decision coverage does not imply achieving 100% of any of these metrics, and vice versa. References = ISTQB® Certified Tester Foundation Level Syllabus v4.0, Test Coverage in Software Testing - Guru99, Structural Coverage Metrics - MATLAB & Simulink - MathWorks India, Test Design Coverage in Software Testing - GeeksforGeeks.

Independent of the software development lifecycle followed, it is a good practice to ensure that each test level (e.g., unit testing, integration testing, system testing) has clear and appropriate objectives. This ensures that the testing is focused and effective at each level. Other options either contradict good practices or are not applicable across all lifecycle models.